System and method for mobile and distributed cloud-centric detection of unmanned systems

ABSTRACT

An unmanned aerial system (UAS) detection device includes a sensor having programmed instructions to cause the sensor to scan energy in an electromagnetic spectrum; process the energy in the electromagnetic spectrum into bursts; determine whether the bursts are valid UAS bursts based on burst criteria; and correlate the bursts into a single signal.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional PatentApplication No. 62/669,621, filed on May 10, 2018, the entire contentsof which are incorporated herein by reference.

BACKGROUND

The present disclosure relates generally to unmanned system detectionand more particularly to a mobile sensor network for detecting,aggregating, and analyzing threats of unmanned systems.

Unmanned systems such as unmanned aerial systems (“UAS”) are becomingincreasingly more prevalent in fields such as imagery, surveying,construction, measurement, and a wide range of other applications.However, accompanying the wide range of great new capabilities enabledby these systems are a new set of potential threats to buildings,facilities, public gatherings such as sporting events or concerts,critical infrastructure, private corporations or individuals, and caneven be used as auxiliary to crime. These risks can include negligentflight, surveillance, physical attack, or other forms of interventionwhich present a fundamentally new challenge to security and publicsafety. To counter these risks, a fundamentally new set of tools areneeded to provide appropriate levels of security against this new classof threats.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a UAS detection system, according to anexemplary embodiment.

FIG. 2 is a block diagram of the sensor as shown in the system of FIG. 1, according to an exemplary embodiment.

FIG. 3 is a block diagram of the cloud aggregator as shown in the systemof FIG. 1 , according to an exemplary embodiment.

FIG. 4 is an illustration of use cases for the system as shown in thesystem of FIG. 1 , according to an exemplary embodiment.

FIG. 5 is a flowchart of a process for detection by the sensor as shownin the system of FIG. 1 , according to an exemplary embodiment.

FIG. 6 is a flowchart of a process of collecting data by the cloudaggregator as shown in the system of FIG. 1 , according to an exemplaryembodiment.

FIG. 7 is a flowchart of a process for detection by the sensor as shownin the system of FIG. 1 , according to an exemplary embodiment.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar components, unless contextdictates otherwise. The illustrative embodiments described in thedetailed description, drawings, and claims are not meant to be limiting.Other embodiments may be utilized, and other changes may be made,without departing from the spirit or scope of the subject matterpresented here. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe figures, can be arranged, substituted, combined, and designed in awide variety of different configurations, all of which are explicitlycontemplated and make part of this disclosure.

Systems that detect both manned and unmanned systems do currently existin the market today. However, these systems tend to be overly complexand carry a high cost of purchase and operation. These sensors are notwidely accessible or practical to security forces with restrictivebudgets. A technical challenge for current systems is to detect unmannedvehicles in an urban environment. The sensors experience severe signalfading when buildings obstruct line-of-sight. Installing a sufficientnumber of legacy sensors can be cost prohibitive. Another technicalchallenge is that current systems lack broader situational awarenesssuch as interconnectivity and are not operated as coherent andcollaborative services. Finally, current systems lack technologyfeaturing an intuitive interface for untrained operators.

Disclosed herein is a network of compact, fixed and mobile, low-costsensing devices, a centralized aggregator for combining and analyzingthe sensor data, and a system for monitoring devices for logging,monitoring, and alerting to operators about identified drones, which arealso known as unmanned systems or unmanned aerial systems (“UAS”).Herein, unmanned system or UAS refers to both the unmanned vehicle aswell as its associated remote control system including the pilot(s)and/or ground control station(s). The disclosure provides a technicalsolution for the technical challenge of detecting unmanned systems invarious environments by providing a low-cost, scalable solution. Forsome situations such as in urban canyons, a sufficient number of sensorscan be deployed, increasing the likelihood that a potential unmannedthreat can be detected. In other situations, mobile sensors enable moreflexible deployment scenarios (mobile security officers and benefitsfrom mobile sensor triangulation) and procurement strategies (poolingresources across venues). The disclosure also offers a technicalsolution of interconnectivity and collaboration. The sensors identifyUAS unique identifiers, which can be used by the aggregator to managethe aggregated data and by the monitoring devices to track threatingUAS. Moreover, the sensors feature configurable software defined radiosand each can be individually allocated to detect many different types ofUAS, and can be remotely updated to detect new types of UAS even aftersensor deployment. Finally, the disclosure provides a technical solutionfor interfacing to untrained operators. The system described offersintuitive user interface that maps the UAS, displays the power levelsand/or estimated distances between sensors and UAS, and providesperformance metrics and historical data.

FIG. 1 is a block diagram of a UAS detection system 100, according to anexemplary embodiment. The UAS detection system 100 is shown to includeone or more sensors 101, a cloud aggregator 102, and one or moremonitoring devices 103, all of which are coupled together by a network104. Additional, fewer, or different blocks may be included depending onthe implementation. Although the UAS detection system 100 embodimenttargets UASs, it is understood that the UAS detection system 100 cantarget other unmanned systems such as the entire unmanned systemsincluding the vehicle as well as its remote control system, and thelike.

The sensor 101 may detect a UAS. In some embodiments, the sensor 101scans an electromagnetic spectrum for signal activity. The scanning maybe implemented using phased-array beam forming and beam steeringtechniques. The sensor 101 may filter and process the signal activity inthe spectrum into bursts that are associated with UAS-related signals.The bursts may be extracted in time and frequency. Methods forextracting include extracting a time series at one or more frequenciesof interest or extracting power at one or more time-intervals and at oneor more frequencies of interest.

The sensor 101 may include a number of signal-specific paths. Eachsignal-specific path can determine whether a received signal is a UASsignal based on attributes of the signal such as signal power level 105,frequency and frequency pattern, burst length, modulation type, burstinterval, and the like. The sensor 101 can generate a score for each ofthe attributes, for each of the sign-specific paths. Responsive to thescore being greater than a predefined threshold, the sensor 101 candetermine that the attribute of the received signal matches an attributeof a UAS signal. Responsive to determining a predefined number ofmatching attributes in one signal specific path, the sensor 101 candetermine that the received signal matches a UAS signal. The sensor 101may measure the UAS signal power level 105 in the signal-specific paths.

There may be collection of methods for different UAS types such asdifferent UAS modulation types. The UAS modulation types includefrequency shift keying (FSK), direct sequence spread spectrum (e.g. codedivision multiple access), orthogonal frequency division multiplexing(“OFDM”), frequency-hopping spread spectrum (“FHSS”), and the like. Insome cases, the signal-specific paths are designated for differentfrequency bands. In some embodiments, a plurality of paths may be on onefrequency band (e.g. both OFDM and FSK may produce energy in the 2.4 GHzband) or one path may cover multiple frequency bands (e.g. FSK producesenergy in 2.4 GHz and 5.9 GHz bands). Each sensor 101 can be configuredfor many different UAS types. For example, a sensor 101 can detect UASsignals of OFDM modulation type at 2.4 GHz and UAS signals of FSK typeat 5.9 GHz. In another example, a first sensor 101 can detect the UASsignals of OFDM modulation type at 2.4 GHz, and a second sensor 101 maydetect the UAS signals of FSK type at 5.9 GHz. In another example, afirst sensor 101 can receive a 2.4 GHz WiFi signal and a first UASsignal. The first sensor 101 can be specified to indicate that a signalis a UAS signal responsive to determining that the signal is at 2.4 GHzand of the OFDM modulation type. Upon processing the 2.4 GHz WiFisignal, the first sensor 101 can indicate that the 2.4 GHz WiFi signalis not a UAS signal responsive to determining that the 2.4 GHz WiFisignal is not of the OFDM type. Upon processing the first UAS signal,the first sensor 101 can indicate that the first UAS signal is a UASsignal responsive to determining that the first UAS signal is at 2.4 GHzand of the OFDM type.

The sensor 101 may correlate the bursts into an aggregate signal basedon the known characteristics of that signal. Correlating the bursts intothe aggregate signal can reduce false alarm rates and documents thepatterns such as the frequency hopping pattern of many UAS-relatedsignals. The sensor 101 may determine a UAS unique identifier 106corresponding to the UAS in response to correlating the bursts into theaggregate signal. The UAS unique identifier 106 may be the frequencyhopping pattern. Some manufacturers hard code the frequency hoppingpattern of each individual radio differently so it is unlikely that twoUASs will interfere with each other. The pattern may be referred to as afingerprint. In some embodiments, the UAS unique identifier 106 is ahardware specific identification such as a media access control (“MAC”)address in Wi-Fi networks, a network specific identification such as anInternet Protocol (“IP”) address, or another unique address identifier.

The sensor 101 can measure its own sensor location 107 and may and mayrely on a GPS hardware module. In some embodiments, the sensor 101 maydetermine a distance relative to the UAS and a direction of the UASrelative to the sensor 101. This may be achieved through a combinationof power-based range estimations, known antenna patterns, and datafusion from multiple sensors within a collaborative network. In otherembodiments, this is achieved through radar techniques and phased-arraybeam steering techniques. In some embodiments, the sensor 101 cancapture an image of the UAS.

In some embodiments, the sensor 101 can send the UAS signal power level105, the corresponding UAS unique identifier 106, and the sensorlocation 107 of the sensor 101 to the cloud aggregator 102 via thenetwork 104. A data point may include a distance of the UAS relative tothe sensor 101, a direction of the UAS relative to the sensor 101, animage of the UAS, an alert message, the UAS signal power level 105, theUAS unique identifier 106, and/or the sensor location 107. In someembodiments, the sensor 101 can send the data point asynchronously tothe cloud aggregator 102. In other embodiments, the sensor 101 can sendthe data point synchronously to the cloud aggregator 102. The sensor 101may send the data point to the cloud aggregator 102 via a local agent ora router.

The sensor 101 may be implemented as a man-portable RF sensing packagedesigned to be easily carried by security personnel on patrol, on mobileoperations, by festival or event operators and security, and the like.In other embodiments, the sensor 101 may be fixed. The sensor 101 may bemounted to a tripod, vehicle, building or other structural object. Thesensor 101 may be associated with a client and a site. In someembodiments, the client is a person, a company, a downstream processingfunction, a law enforcement office, or the like. The sensor 101 may beassigned to multiple clients. The client may have full access to thesensor 101 or partial access to the sensor 101. The site may be acollection of sensors 101 within a geographic boundary or with someother common feature.

The cloud aggregator 102 may collect the data point from the sensor 101via the network 104. The cloud aggregator 102 may collect a plurality ofdata points from a plurality of sensors 101 via the network 104. Thecloud aggregator 102 can collect the data points in a way that avoidscollision. Each of the data points may be encoded into a data signal. Insome embodiments, each of the data signals of the respective sensor 101may be associated with a unique code. The cloud aggregator 102 candecode each of the data signals by applying the respective codecorresponding to the respective sensor 101. In some embodiments, thecloud aggregator 102 may assign time slots to each of the sensors 101and each sensor 101 may send its data signal (e.g. its data point)during the respective timeslot. In some embodiments, the cloudaggregator 102 may assign a different frequency channel to each of thesensors 101 and each sensor 101 may send its data signal at theallocated frequency.

The cloud aggregator 102 can select a subset of data points which havethe same UAS unique identifier 106. The cloud aggregator 102 maycalculate a UAS location 108 based on the selected subset of data pointshaving the same UAS unique identifier 106. In some embodiments, thecloud aggregator 102 may use trilateration or triangulation techniquesto calculate the UAS location 108. Trilateration or triangulationtechniques may include linear regression or non-linear regression. Thecloud aggregator 102 may increase the accuracy of determining the UASlocation 108 by collecting a higher number of data points correspondingto the UAS unique identifier 106 or by collecting data points fromsensors 101 that are closer to the UAS that corresponds with the UASunique identifier 106. The cloud aggregator 102 select a subset of thedata points based on the corresponding power levels 105. The cloudaggregator 102 may select a first data point responsive to the powerlevel 105 included in the data point being greater than a firstpredefined power threshold. The cloud aggregator 102 may discard a firstdata point responsive to the power level 105 included in the first datapoint being less than a second predefined power threshold. The cloudaggregator 102 may perform relation or triangulation using only thesubset of the data points. In some embodiments, the cloud aggregator 102may assign weights to the data points based on the corresponding powerlevels 105. The cloud aggregator 102 may assign a first weightresponsive to the power level 105 included in the data point beinggreater than a first predefined power threshold. The cloud aggregator102 may assign a second weight responsive to the power level 105included in the first data point being less than a second predefinedpower threshold. The cloud aggregator 102 can compute a weightedtrilateration or triangulation function (e.g. weighted least squarestrilateration) using the weights. In the least squares approach, eachdata point may have a corresponding error, and the cloud aggregator 102may compute a weighted error as a product of the error and thecorresponding weight determined based on the power level 105.

The cloud aggregator 102 can determine additional UAS information basedon the unique identifier 106. The additional UAS information couldinclude UAS brand, size, weight, top speed, typical use or how likelythey are in a certain area, and information to potentially help asecurity officer quickly learn to pilot the UAS if control of the UASmust be physically taken from the pilot. In some embodiments, the cloudaggregator 102 can access an internet search engine and search for theadditional UAS information using inferred UAS information as keywords.In other embodiments, the search may use the unique identifier 106 as akeyword. In other embodiments, the cloud aggregator 102 may use featurerecognition to extract the additional UAS information from the receivedimage of the UAS.

The cloud aggregator 102 may authenticate the sensor 101 by itsuniversal unique identifier (“UUID”), which is a unique alpha-numericstring. In some embodiments, the cloud aggregator 102 can authenticatethe sensor 101 by the UUID of the client associated with the sensor 101.The cloud aggregator 102 may authenticate the sensor 101 by the UUID ofthe site associated with the sensor 101. The cloud aggregator 102 cansend the UAS location 108 to the monitoring device 103. In someembodiments, the cloud aggregator 102 can send the additional UASinformation to the monitoring device 103.

The monitoring device 103 can receive the UAS location 108 and the UASthe additional UAS information. The monitoring device 103 can monitorthe detected UAS. The monitoring device 103 may have a user interface.The user interface is designed to be simple for untrained users tooperate. The user interface is further described as a block in thesensor 101 in FIG. 2 .

The monitoring device 103 can be implemented as a cellular phone, atablet, a laptop, a desktop, and the like. In some embodiments, themonitoring device 103 may be implemented as an on premise monitoringcenter such as a security operations center (e.g. for a stadium, mall,office building, and the like). In other embodiments, the monitoringdevice 103 may be implemented as a remote operations center.

The network 104 may comprise a local area network (“LAN”), a wirelessLAN (“WLAN”), or wide area network (“WAN”). The network 104 may comprisea heterogeneous collection of networking links including a on premisenetwork infrastructure such as a local network and WANs such as longterm evolution (“LTE”), LTE-Unlicensed (“LTE-U”), Global System forMobile communications (“GSM”) and low-power WAN (“LPWAN”). The topologyof the network 104 can be fixed within a deployed area, or it can bechanging due to the mobile nature of the sensors 101. The topology canbe a hybrid of the fixed sensors 101 and the mobile sensors 101. Variousranges and detection coverage areas could apply depending on intendeddeployment scenario where some sensors 101 could be targeted at longranges in some direction or omni-directional in all directions. Thearchitecture of the network 104 may be implemented as a traditionalhub-and-spoke architecture with the cloud aggregator 102 being the huband each sensor 101 being a spoke.

FIG. 2 is a block diagram of the sensor 101 as shown in the system 100of FIG. 1 , according to an exemplary embodiment. The sensor is shown toinclude an antenna 205, a software defined radio (“SDR”) 210, a digitalsignal processor (“DSP”) 215, a network transceiver 220, a globalpositioning system (“GPS”) module 225, a general processor 230, a memory240, a user interface 255, and a bus 260. The bus 260 couples togetherthe DSP 215, the network transceiver 220, the GPS module 225, thegeneral processor 230, the memory 240 and the user interface 255.Additional, fewer, or different blocks may be included depending on theimplementation.

The antenna 205 may receive the energy from the electromagneticspectrum. The antenna 205 configuration may include directive antennassuch as patch antennas, Yagi antennas, phased-array antennas,electronically steerable antennas, and the like. The antenna 205 mayprovide spatially focused sensing performance. For example, in a case ofan airport deployment, the directional antennas 205 may point down arunway approach path.

The SDR 210 is a radio communication system coupled to the antenna 205.The SDR 210 can amplify, translate and filter the energy received by theantenna 205. The SDR 210 may have multiple paths that translatedifferent frequency bands down to bands centered at 0 Hz or at a fixedoffset from 0 Hz. Each path may include a passive or active mixer totranslate the corresponding frequency band. Each mixer may be driven bya local oscillator. The SDR 210 may convert the energy from an analogdomain into a digital domain. In some embodiments, the SDR 210 processesand correlates the energy. The SDR 210 may be re-configurable based oninputs from the DSP 215. One purpose of having the SDR 210 is that thecloud aggregator 102 or the sensor 101 may configure the SDR 210 toreceive specific UAS signals communicating at specific frequency bandsor using specific modulation types. Another purpose is for the cloudaggregator 102 or the sensor 101 to increase or decrease the range ofthe SDR 210. The SDR 210 may be implemented as an field-programmablegate array (“FPGA”).

The DSP 215 is coupled to the SDR 210. The DSP 215 may filter andprocess the energy in the spectrum into bursts that are associated withUAS-related signals. In some embodiments, the DSP 215 can pass thebursts to a number of signal-specific paths which determine whether ornot sufficient criteria are met such as signal power level 105,bandwidth, carrier frequency, frequency pattern, burst length,modulation type, burst interval, packet format, and the like. The DSP215 can determine whether a criterion is met by generating a score anddetermining whether the score is greater than a threshold. The DSP 215may compare the burst or the aggregation of bursts against a frequencydomain mask to determine if the bandwidth matches a bandwidth of the UASsignal. The DSP 215 may compare the burst or the aggregation of burstsagainst a time-domain mask to determine if the burst length and theburst interval matches the burst length and the burst interval of theUAS signal.

The DSP 215 may demodulate the bursts into symbols. The symbols may havea real part and an imaginary part. The symbols may include ones andzeros. The DSP 215 can determine whether a modulation received signalmatches a modulation of the UAS signal by comparing the symbolconstellation of the received signal and the expected symbolconstellation of the UAS signal. The DSP 215 may determine an errorvector magnitude (“EVM”). If the EVM is below a predefined threshold,the received modulation type matches a UAS modulation type. If the EVMis above a predefined threshold, the received modulation type does notmatch a UAS modulation type.

The DSP 215 may correlate the bursts into an aggregate signal based onthe known characteristics of that signal. The bursts may have uniquetiming or frequency channels. Correlation may be implemented using arake-receiver. In some embodiments, the DSP 215 can determine a UASunique identifier 106 corresponding to the UAS in response tocorrelating the bursts into the aggregate signal. The UAS uniqueidentifier 106 may include a modulation pattern, frequency hoppingpattern, or a hardware identifier such as a MAC address or otheraddressing identifier.

The network transceiver 220 may send the data point including the UASsignal power level 105, the unique identifier 106, and the sensorlocation 107 to the cloud aggregator 102. The data point may include atleast one of the distance of the UAS relative to the sensor 101, thedirection of the UAS relative to the sensor 101, the image of the UAS,and the alert messages. In some embodiments, the network transceiver 220can send a request to cloud aggregator 102 to access the network 104.The network transceiver 220 may encode or append its UUID into therequest to be sent. In some embodiments, the network transceiver 220 cansend the UUID of the client associated with the sensor 101 or the UUIDof the site associated with the sensor 101. The network transceiver 220may use Wi-Fi, cellular LTE or other similar network connection. Thenetwork transceiver 220 may be implemented as an FPGA or as anapplication-specific integrated circuit (“ASIC”).

The GPS module 225 may determine the sensor location 107 of the sensor101. The GPS module 225 may be implemented as an ASIC. The generalprocessor 230 can be configured store data, fetch data and executeapplications, code and/or instructions stored in the memory 240. Thegeneral processor 230 may be implemented as an integrated circuit.

The memory 240 can store data, applications, code and/or instructionsfor execution by the processor 230. The memory 240 may include localmemory such as cache, and secondary memory such as hard disk drive(“HDD”), solid-state drive (“SSD”), remote storage, cloud storage, andnetwork storage. Both local memory and secondary memory can includerandom access memory (“RAM”) and different types of read-only memory(“ROM”).

The memory 240 is shown to include a sensor controller 245 and adetection database 250. The detection database 250 may store localrecent-detection data to be sent to the cloud aggregator 102. The sensorcontroller 245 may control and update at least one of the antenna 205,the SDR 210, and the DSP 215. In some embodiments, the sensor controller245 may send commands to the antenna 205 to beam-steer. In otherembodiments, the sensor controller 245 may send commands to the SDR 210to translate a different frequency band down to 0 Hz. In yet otherembodiments, the sensor controller 245 may send commands to the DSP 215to change the demodulating scheme, such as from quadrature amplitudemodulation (“QAM”) to OFDM. The sensor controller 245 may be implementedas instructions stored on the memory 240 and executed by the generalprocessor 230 in order to perform operations specified by the sensorcontroller 245. In some embodiments, the sensor controller 245 isimplemented as a computing device.

The user interface 255 may display a map in some graphicalrepresentation. The user interface 255 may display one or more UASlocations 108 as one or more circles or shapes reflecting actual antennapatterns with one or more diameters and one or more colors for coding.As the UAS location 108 becomes more accurate due to more sensors 101detecting the UAS or because the sensors 101 are approaching the UAS,the diameter of the circle or range of the antenna pattern shape willadjust accordingly. In some embodiments, the user interface 255 candisplay an estimate of the distance between sensor and UAS, which mayinclude signal strength, such as a signal power level 105, in decibels,of the UAS in relation to a location of the sensor 101 containing theuser interface 255. The user interface 255 may display one or moresensor locations 107. In some embodiments, the user interface 255 candisplay the health of sensors 101. The user interface 255 may displaydetection metrics such as the signal power level 105, frequency ofoperation, modulation type, distance, the UAS location 108, location ofpilot, alert level, and the like. The user interface 255 may receiveuser inputs to track or alert the user if a specified UAS is detected.In some embodiments, the user interface 255 can receive inputs towhitelist specific UASs.

In some embodiments, the user interface 255 may be implemented as anapplication or a set of instructions that is downloaded, stored onto thememory 240, and executed by the general processor 230 to performoperations of the user interface 255. In other embodiments, the userinterface 255 may be implemented as a web-based user interface 255. Inyet other embodiments, the user interface 255 can be implemented as acloud-based Infrastructure-as-A-Service provider (e.g. Amazon AWS). Inyet other embodiments, the user interface 255 is a stand-alone computingdevice that may be connected to the sensor 101. In some embodiments, theuser interface may consist of a combination of lights and sounds whichcommunicate simple operating conditions or alert conditions to the userwithout the need for a graphical user interface.

Each of the components of the sensor 101 (e.g. the SDR 210, the DSP 215,the network transceiver 220, the GPS module 225, the sensor controller245, and the user interface 255) is implemented using hardware or acombination of hardware or software, in one or more embodiments. Each ofthe components can include circuitry such as CMOS transistors or BJTtransistors. Each of the components can include any application,program, library, script, task, service, process or any type and form ofexecutable instructions executing on hardware of the sensor 101. Thehardware includes circuitry such as one or more processors (e.g. thegeneral processor 230) in one or more embodiments. In some embodiments,one or more components (e.g. the DSP 215) has its own dedicatedprocessor. Each of the one or more processors is hardware.

FIG. 3 is a block diagram of the cloud aggregator 102 as shown in thesystem 100 of FIG. 1 , according to an exemplary embodiment. The cloudaggregator 102 is shown to include a network transceiver 305, a generalprocessor 310, a memory 315, a user interface 335, and a bus 340. Thebus 340 couples together the network transceiver 305, the generalprocessor 310, the memory 315, and the use interface 335. Additional,fewer, or different blocks may be included depending on theimplementation.

The network transceiver 305 may receive the data point including the UASsignal power level 105, the unique identifier 106, and the sensorlocation 107 from the sensor 101. The data point may include at leastone of the distance of the UAS relative to the sensor 101, the directionof the UAS relative to the sensor 101, the image of the UAS, and thealert messages from the sensor 101. The network transceiver 305 mayreceive a plurality of data points from a plurality of sensors 101. Insome embodiments, the network transceiver 305 can receive a request fromthe sensor 101 to access the network 104. The network transceiver 220may use Wi-Fi, cellular LTE or other similar network connectionincluding proprietary hosted networks such as police radio networks. Thenetwork transceiver 305 may be implemented as an FPGA or as an ASIC.

The general processor 310 can be configured store data, fetch data andexecute applications, code and/or instructions stored in the memory 315.The general processor 310 may be implemented as an integrated circuit.

The memory 315 can store data, applications, code and/or instructionsfor execution by the processor 310. The memory 315 may include localmemory such as cache, and secondary memory such as hard disk drive(“HDD”), solid-state drive (“SSD”), remote storage, cloud storage, andnetwork storage. Both local memory and secondary memory can includerandom access memory (“RAM”) and different types of read-only memory(“ROM”). The memory 315 is shown to include a data manager 320, adetection database 325, and a network controller 330. The detectiondatabase 325 may store a plurality of data points received from theplurality of sensors 101. In some embodiments, the detection database325 may include a lookup table (“LUT”).

The data manager 320 organizes the plurality of data points into subsetsso that each subset includes data points having the same UAS uniqueidentifier 106. In some embodiments, each subset is assigned a differentrange of physical addresses in the detection database 325 of the memory315. The UAS unique identifier 106 and the subset can be mapped in theLUT. For example, the UAS unique identifier 106 can be stored in anindex in a first array of the LUT. The same index in the second array ofthe LUT can store the first address of the subset of the data pointscorresponding to the UAS unique identifier 106. In some embodiments, thedata manager 320 calculates the UAS location 108 based on the subset ofdata points having the same UAS unique identifier 106. The calculationmay be implemented by averaging the weighted sensor locations 107 of thedata points corresponding to the UAS identifier 106. In someembodiments, each weight of each sensor location 107 is based on atleast one of the UAS signal power level 105, the distances of the UASrelative to the sensor 101, and the direction of the UAS relative to thesensor 101. The data manager 320 may be implemented as instructionsstored on the memory 315 and executed by the general processor 310 inorder to perform operations specified by the data manager 320. In someembodiments, the data manager 320 is implemented as a computing device.

The network controller 330 may assign the sensors 101 to UAS types basedon criteria such as modulation type or frequency range. In someembodiments, the network controller 330 can assign different sensors 101to different UAS types. For example, the network controller 330 canassign a first sensor 101 to detect UAS signals of OFDM modulation typeat 2.4 GHz, and a second sensor 101 to detect UAS signals of frequencyhopping type at 5.9 GHz. The network controller 330 may decode the UUIDof the sensor 101 and compare it to a list of permitted UUIDs withaccess types. The access types include administrative access, datasharing access, anonymized data access, and the like. If the networkcontroller 330 finds a match between the UUID of the sensor 101 and oneof the permitted UUIDs, the network controller 330 grants the accesstype associated with the permitted UUID that matches the UUID of thesensor 101. The network controller 330 may be implemented asinstructions stored on the memory 315 and executed by the generalprocessor 310 in order to perform operations specified by the networkcontroller 330. In some embodiments, the network controller 330 isimplemented as a computing device.

The user interface 335 may display a map in some graphicalrepresentation. The user interface 335 may display one or more UASlocations 108 as one or more circles or shapes reflecting antennapattern with one or more diameters or sizes and with one or more colors.In some embodiments, the user interface 335 is the user interface 255 asshown in FIG. 2 . In some embodiments, the user interface 335 may beimplemented as an application or a set of instructions that isdownloaded, stored onto the memory 315, and executed by the generalprocessor 310 to perform operations of the user interface 335. In otherembodiments, the user interface 335 may be implemented as a web-baseduser interface 335. In yet other embodiments, the user interface 335 canbe implemented as a cloud-based Infrastructure-as-A-Service provider(e.g. Amazon AWS). In yet other embodiments, the user interface 335 is astand-alone computing device that may be connected to the cloudaggregator 102.

Each of the components of the cloud aggregator 102 (e.g. the networktransceiver, the data manager 320, the network controller 330, and theuser interface 335) is implemented using hardware or a combination ofhardware or software, in one or more embodiments. Each of the componentscan include circuitry such as CMOS transistors or BJT transistors. Eachof the components can include any application, program, library, script,task, service, process or any type and form of executable instructionsexecuting on hardware of the cloud aggregator 102. The hardware includescircuitry such as one or more processors (e.g. the general processor310) in one or more embodiments. Each of the one or more processors ishardware.

FIG. 4 is an illustration of use cases for the system 100 as shown inthe system of FIG. 1 , according to an exemplary embodiment. Deploymentoptions may include handheld 401 sensors 101 and belt-worn 402 sensors101. In some embodiments, the deployment options include sensors 101mounted in a whitelisted UAS 403 and vehicle mounted 405 sensors 101.The deployment options may include sensors 101 affixed to an existingstructure 404. The deployment options may include any combination ofthese. The sensor 101 may be operated manually, such as the handheldsensor, or may be managed by an automated local or central system, suchas an asset security monitoring system at an event 406. UAS componentsdetected may include the unmanned vehicle or its control systemincluding pilot or ground station.

FIG. 5 is a flowchart of a process 500 for detection by a first sensor101 like the sensor 101 as shown in the system 100 of FIG. 1 , accordingto an exemplary embodiment. Additional, fewer, or different steps may beincluded depending on the implementation. At step 510, the first sensor101 may detect a first UAS having a first signal power level 105. Atstep 520, the first sensor 101 can determine a first UAS uniqueidentifier 106 corresponding to the first UAS. At step 530, the firstsensor 101 can self-measure a first sensor location 107. At step 540,the first sensor 101 may send the first UAS signal power level 105, thecorresponding first UAS unique identifier 106, and the first sensorlocation 107 to the cloud aggregator 102.

FIG. 6 is a flowchart of a process 600 of collecting data by the cloudaggregator 102 as shown in the system of FIG. 1 , according to anexemplary embodiment. In some embodiments, the process 600 starts inresponse to the last step of the process 500. Additional, fewer, ordifferent steps may be included depending on the implementation. At step610, the cloud aggregator 102 can collect a plurality of UAS powerlevels 105, a plurality of UAS unique identifiers 106, and a pluralityof sensor locations 107 from a plurality of sensors 101 including thefirst sensor 101. At step 620, the cloud aggregator 102 may select thefirst unique identifier 106 of the plurality of unique identifiers 106.At step 630, the cloud aggregator 102 may calculate a first UAS location108 based on each of the plurality of UAS power levels 105 correspondingto the first unique identifier 106 and each of the plurality of sensorlocations 107 corresponding to the first unique identifier 106. In someembodiments, the cloud aggregator 102 sends the first UAS location 108to the monitoring device 103.

FIG. 7 is a flowchart of a process 700 for detection by the sensor 101as shown in the system of FIG. 1 , according to an exemplary embodiment.Additional, fewer, or different steps may be included depending on theimplementation. At step 710, the sensor 101 can scan energy in anelectromagnetic spectrum. At step 720, the sensor 101 may filter andprocess the energy in the electromagnetic spectrum into bursts. At step730, the sensor 101 can determine whether the bursts are valid UASbursts based on attributes. The sensor 101 can determine whether thebursts are the valid UAS bursts by determining whether the attributes ofthe bursts match the corresponding attributes of the UAS bursts. In someembodiments, the attributes include one or more of frequency, frequencypattern, burst length, modulation type, burst interval, and the like. Atstep 740, the sensor 101 may correlate the bursts into a singleaggregate signal. The sensor 101 may correlate the bursts havingattributes that match attributes of a common UAS burst type.

The construction and arrangement of the systems and methods as shown inthe various exemplary embodiments are illustrative only. Although only afew embodiments have been described in detail in this disclosure, manymodifications are possible (e.g., variations in sizes, dimensions,structures, shapes and proportions of the various elements, values ofparameters, mounting arrangements, use of materials, colors,orientations, etc.). For example, the position of elements may bereversed or otherwise varied and the nature or number of discreteelements or positions may be altered or varied. Accordingly, all suchmodifications are intended to be included within the scope of thepresent disclosure. The order or sequence of any process or method stepsmay be varied or re-sequenced according to alternative embodiments.Other substitutions, modifications, changes, and omissions may be madein the design, operating conditions and arrangement of the exemplaryembodiments without departing from the scope of the present disclosure.

The present disclosure contemplates methods, systems and programproducts on any machine-readable media for accomplishing variousoperations. The embodiments of the present disclosure may be implementedusing existing computer processors, or by a special purpose computerprocessor for an appropriate system, incorporated for this or anotherpurpose, or by a hardwired system. Embodiments within the scope of thepresent disclosure include program products comprising machine-readablemedia for carrying or having machine-executable instructions or datastructures stored thereon. Such machine-readable media can be anyavailable media that can be accessed by a general purpose or specialpurpose computer or other machine with a processor. By way of example,such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROMor other optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to carry or storedesired program code in the form of machine-executable instructions ordata structures and which can be accessed by a general purpose orspecial purpose computer or other machine with a processor. Wheninformation is transferred or provided over a network or anothercommunications connection (either hardwired, wireless, or a combinationof hardwired or wireless) to a machine, the machine properly views theconnection as a machine-readable medium. Thus, any such connection isproperly termed a machine-readable medium. Combinations of the above arealso included within the scope of machine-readable media.Machine-executable instructions include, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing machines to perform a certain function orgroup of functions.

Although the figures show a specific order of method steps, the order ofthe steps may differ from what is depicted. Also two or more steps maybe performed concurrently or with partial concurrence. Such variationwill depend on the software and hardware systems chosen and on designerchoice. All such variations are within the scope of the disclosure.Likewise, software implementations could be accomplished with standardprogramming techniques with rule based logic and other logic toaccomplish the various connection steps, processing steps, comparisonsteps and decision steps.

What is claimed:
 1. A method comprising: scanning, by a sensor, energy generated in an electromagnetic spectrum by one or more unmanned aerial systems (UASs); processing, by the senor, the energy in the electromagnetic spectrum into bursts; determining, by the sensor, characteristics of the bursts, including frequency patterns of the bursts; comparing, by the sensor, the characteristics of the bursts with criteria associated with UAS bursts, wherein the criteria include frequency patterns associated with UAS bursts; determining, by the sensor, that the bursts are UAS bursts based on the characteristics of the bursts matching the criteria associated with UAS bursts; and correlating, the bursts into a single signal based on the matching criteria.
 2. The method of claim 1, further comprising detecting a first UAS having a first UAS signal power level.
 3. The method of claim 2, further comprising determining a first UAS unique identifier corresponding to the first UAS.
 4. The method of claim 3, wherein the first UAS unique identifier is a media access control (MAC) address.
 5. The method of claim 2, further comprising measuring a first sensor location.
 6. The method of claim 1, wherein the scanning uses phased-array beam forming and beam steering.
 7. The method of claim 1, further comprising passing, by the sensor, the bursts to a number of signal-specific paths to determine if a threshold is met.
 8. An unmanned aerial system (UAS) detection device comprising: a sensor having programmed instructions to cause the sensor to: scan energy generated in an electromagnetic spectrum by one or more unmanned aerial systems (UASs); process the energy in the electromagnetic spectrum into bursts; determine, by the sensor, characteristics of the bursts, including frequency patterns of the bursts; compare, by the sensor, the characteristics of the bursts with criteria associated with UAS bursts, wherein the criteria include frequency patterns associated with UAS bursts; determine that the bursts are UAS bursts based on the characteristics of the bursts matching the criteria associated with UAS bursts; and correlate the bursts into a single signal based on the matching criteria.
 9. The device of claim 8, wherein the sensor further includes programmed instructions to cause the sensor to detect a UAS having a UAS signal power level.
 10. The device of claim 8, wherein the sensor receives a sensor location from a GPS module.
 11. The device of claim 8, wherein the sensor further includes programmed instructions to pass the bursts to a number of signal-specific paths to determine if a threshold is met.
 12. A system comprising: a cloud aggregator coupled to a network; a first sensor coupled to the cloud aggregator via the network, wherein the first sensor includes programmed instructions to cause the sensor to: detect a first unmanned aerial system (UAS) having a first UAS signal power level by comparing frequency patterns of the first UAS to known frequency patterns associated with UAS signals; determine a first UAS unique identifier corresponding to the first UAS, wherein the first UAS unique identifier comprises a frequency-hopping pattern of the UAS; measure a first sensor location; and send the first UAS signal power level, the first UAS unique identifier, and the first sensor location to the cloud aggregator, wherein the cloud aggregator calculates a first UAS location based on at least the first sensor location as measured by the first sensor.
 13. The system of claim 12, wherein the cloud aggregator calculates the first UAS location based on a plurality of UAS signal power levels corresponding to the first UAS unique identifier and a plurality of sensor locations corresponding to the first UAS unique identifier.
 14. The system of claim 12, wherein the cloud aggregator collects the plurality of UAS signal power levels, a plurality of UAS unique identifiers, and the plurality of sensor locations from a plurality of sensors including the first sensor.
 15. The system of claim 12, wherein the cloud aggregator selects the first UAS unique identifier.
 16. The system of claim 12, wherein the first sensor includes programmed instructions to cause the first sensor to scan energy in an electromagnetic spectrum and process the energy in the electromagnetic spectrum into bursts.
 17. The system of claim 16, wherein the first sensor further includes programmed instructions to determine whether the bursts are valid unmanned aerial system (UAS) bursts based on burst criteria.
 18. The system of claim 17, wherein the first sensor further includes programmed instructions to correlate the bursts into a single signal.
 19. The system of claim 18, wherein the first sensor further includes programmed instructions to pass the bursts to a number of signal-specific paths to determine if a threshold is met.
 20. The method of claim 2, further comprising calculating, based on the first UAS signal power level, a location of the first UAS and displaying, on a graphical user interface (GUI), the location of the first UAS. 